Prediction accuracy of bivariate score-driven risk premium and volatility filters: an illustration for the Dow Jones
Alvaro Escribano () and
Szabolcs Blazsek ()
UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía
In this paper, we introduce Beta-t-QVAR (quasi-vector autoregression) for the joint modelling of score-driven location and scale. Asymptotic theory of the maximum likelihood (ML) estimatoris presented, and sufficient conditions of consistency and asymptotic normality of ML are proven. Forthe joint score-driven modelling of risk premium and volatility, Dow Jones Industrial Average (DJIA)data are used in an empirical illustration. Prediction accuracy of Beta-t-QVAR is superior to theprediction accuracies of Beta-t-EGARCH (exponential generalized AR conditional heteroscedasticity),A-PARCH (asymmetric power ARCH), and GARCH (generalized ARCH). The empirical results motivate the use of Beta-t-QVAR for the valuation of DJIA options.
Keywords: Generalized; Autoregressive; Score; Dynamic; Conditional; Score; Risk; Premium; Volatility (search for similar items in EconPapers)
JEL-codes: C22 C58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:31339
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